Demographic and population-based analysis of traumatic injury transport outcomes and health-care infrastructure in Northern Québec's rural communities
Notice bibliographique
Résumé
Introduction: North American and international studies have shown mortality and morbidity rates from traumatic injury to be higher in remote and rural populations when compared to urban areas. Little research is explores the available health infrastructure and outcomes of traumatic injuries in such regions, which include Northern regions of Canada (especially British Columbia and Québec), the rural outback of Australia, remote regions in Norway, and very isolated areas in the U.S, amongst others. In isolated Northern Québec communities, transport to the McGill University Health Centre (MUHC), a level-I regional trauma centre is the only option for complex trauma care. This study aims to provide: (1) a demographic analysis of the Northern Québec region, with an emphasis on characterizing the available health care infrastructure; (2) the mechanisms and rates of injuries in the North that require transfer; (3) transfer times and outcomes in patients with traumatic injury from this region.Methods: A manuscript focusing on trauma patient transport outcomes from Northern Québec is incorporated into this thesis. For this portion, quantitative data from trauma cases transferred to MUHC from Northern Québec was obtained from the MUHC trauma registry (Jan 1, 2005 to Dec 31, 2009). Demographic and health services data was obtained from the Reseau universaitaire integre de santé de l'Universite McGill (RUIS), the administrative coordinator of health and trauma services in Northern Québec. We identified mechanisms of injury, transfer times, and survival results in trauma patients transported to the MUHC from Northern Québec and compared the results to a population of trauma patients transported from Montreal's local suburban hospitals.Results: Pertinent literature was identified and summarized to provide an overview of rural and remote trauma experience. Emphasis was placed on ecologic analysis of rural trauma outcomes, use of geographic mapping systems and other scores to quantify remoteness, and a descriptive comparison of rural trauma experiences in Canada, Australia, and Norway. Assessment of the Northern Québec trauma experience revealed that the MGH received 9952 traumas during the study period. 254 of these patients were from the North and had an ISS above 15. 1027 patients with an ISS above 15 were transported from local suburban hospitals. The mean age for the local transport groups was > 40 years and form the North it was > 30. Both groups had a predominantly male population, the majority of whom had sustained blunt trauma. Motor Vehicle Collision was the most common mechanism in the Northern Québec population, averaging 40%. Penetrating trauma was the cause of 21.7% of all transports from Northern Québec, whereas it represented 12.5% of the injuries seen in the local transport population. Patients transferred from the Northern region with an ISS > 15 had a significantly higher mortality rate.Conclusion: Despite the selection and referral biases inherent in observational data of this type, the higher mortality rate observed in patients transferred from Northern Québec likely reflects challenges in timely transport and advanced care. Improved outcomes may result from enhanced/systematic training of local care providers, improved triage and rapid transport protocols.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».